A portable bolt-loosening detection system with piezoelectric-based nondestructive method and artificial neural networks

Na, Wongi S.

doi:10.1177/14759217211008619

Cited by 19 publications

(11 citation statements)

References 26 publications

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“…e sensor selected is the ADXL321MEMS acceleration sensor. However, due to the limitations of the experimental conditions and the completion of the project, the actual aircraft condition monitoring experiment has not been carried out at present [5]. Hu et al adopted the wireless sensor network mechanical equipment monitoring system designed by themselves and applied it to 4 QUY50C crawler cranes.…”

Section: Literature Reviewmentioning

confidence: 99%

Mechanical Vibration Test Based on the Wireless Vibration Monitoring System

Niu¹

2022

Security and Communication Networks

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In order to apply wireless sensor networks to mechanical vibration monitoring, the author proposes a wireless network topology with multiple data collection points for mechanical vibration monitoring. This structure reduces the transmission load of the data collection point, increases the data transmission rate of the network, balances the energy dissipation in the network, and utilizes the general wireless sensor network hardware platform. The network transmission protocol and related auxiliary mechanisms are designed and implemented, and a wireless vibration monitoring test platform is constructed. The transmission performance of the network structure with multiple data collection points is evaluated through the actual test. The experimental results show that by using the wireless sensor network topology with multiple data collection points, it can meet the requirements of continuous transmission of vibration data obtained by 1 kHz sampling. Conclusion. The system performance of the wireless sensor network based on this network structure has been improved under the condition of general hardware, and the network structure of multiple data collection points shows good performance in the process of high-speed data transmission.

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Section: Literature Reviewmentioning

confidence: 99%

Mechanical Vibration Test Based on the Wireless Vibration Monitoring System

Niu¹

2022

Security and Communication Networks

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“…They concluded that the accuracy of PNN improved from 72.2% to 94.4% by smoothing the signal using the centered moving average method with 20 data points. Further, Na 145 created a low-cost monitoring system using a re-attachable PZT device in conjunction with a PNN to detect bolt looseness. They extracted impedance signals from three different-sized bolts and quantified them using statistical metrics, that is, RMSD and MAPD.…”

Section: Mechanical Properties Ofmentioning

confidence: 99%

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Chelimilla

Chinthapenta

Kali

et al. 2023

Structural Health Monitoring

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The integrity of bolted joints is still a challenging problem owing to the gross or localized slip at the interfacial surfaces of the joints when subjected to external disturbances such as vibrations. This slip escalates the interfacial movement and, thus, leads to a decrease in preload levels, that is, looseness of the bolted assemblies. In the last decade, modal analysis, wave propagation, and percussion methods were traditionally used to detect looseness in bolted connections. With an increase in computational power, machine learning algorithms such as neural networks, random forests, decision trees, and support vector machines complemented the traditional methods in accurate looseness estimation. Subsequently, this integration paved the path for real-time health monitoring of bolted joints. This paper summarizes recent investigations on looseness detection in bolted assemblies based on traditional methods and machine learning algorithms. The working principle, advantages, challenges, and applications of the aforementioned methods are also detailed in this paper. Apart from these investigations, the latest studies on the Internet of Things-based health monitoring of structures are also reviewed to explore their adaptability in remote monitoring of bolted connections for damage detection in the future.

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“…20 Among them, piezoelectric sensors based on the piezoelectric effect have become one of the key SHM technologies owing to their high sensitivity, simple control system, light weight, low energy consumption, as well as dual sensing and actuation. 21,22 Thanks to the superior performance of piezoelectric sensing technology, the research on health monitoring of bolted joints supported by the piezoelectric vibration method, 23 ultrasonic guided wave (UGW) technique, 24 and electromechanical impedance (EMI) method 25 has burst into a series of valuable achievements in recent years. 26–36 Earlier, research on piezoelectric sensing-based health monitoring techniques for bolted joints focused on investigating the feasibility and effectiveness of piezoelectric sensing in various modes of damage monitoring.…”

Section: Introductionmentioning

confidence: 99%

A novel damage index integrating piezoelectric impedance and ultrasonic guided wave for damage monitoring of bolted joints

Liao

Sun

Wang

et al. 2023

Structural Health Monitoring

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The structural safety of bolted joints, one of the typical connection techniques utilized in engineering applications, is threatened by the harsh service environment. Herein, a novel damage index (DI) integrating piezoelectric impedance and ultrasonic guided wave is proposed for satisfying the damage monitoring needs of bolted joints. By integrating the two sensing techniques, the shortcomings of a single sensing technique in terms of signal interpretation, stability, and structural adaptability are eliminated. The novel damage severity factor DIZ-V is formed by fusing the damage indices DIZ and DIV weighted by multi-class DIs of the two sensing techniques. Thanks to DIZ-V and a damage mode identification factor presented in this paper, the damage modes and damage severity of early-stage bolt loosening and crack damage are identified. Furthermore, a novel dynamic superposition model (DSM) based on a dynamic-update baseline is developed to address the deficiency that the static baseline method (SBM) has. SBM utilizes the undamaged state signal as the baseline and has the problem of being prone to failure in the later-stage of monitoring. Thus, the phenomenon of nonlinear variation of DIZ-V caused by damage deterioration was avoided via DSM optimization. The feasibility and effectiveness of the proposed method were verified by conducting a series of practical damage identification tests on aluminum bolted joints. And the results demonstrated that the proposed technique could not only identify the severity and mode of damage, but also has excellent reliability. Therefore, the novel damage monitoring technique for bolted joints is a good complement to the existing techniques, which can provide a new strategy for damage monitoring.

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A portable bolt-loosening detection system with piezoelectric-based nondestructive method and artificial neural networks

Cited by 19 publications

References 26 publications

Mechanical Vibration Test Based on the Wireless Vibration Monitoring System

Mechanical Vibration Test Based on the Wireless Vibration Monitoring System

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

A novel damage index integrating piezoelectric impedance and ultrasonic guided wave for damage monitoring of bolted joints

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